Lead Generative AI Engineer-Player Coach

AcquireX; Pune District; On-site; INR 2,500,000 - 3,500,000; Full time

Job summary

A leading technology firm in Pune is seeking a Lead Generative AI Engineer to own the technical vision of their generative AI products. The role involves leading a team to design and deploy advanced AI systems, mentoring engineers, and developing innovative solutions powered by LLM technologies. Ideal candidates will have extensive experience in production LLM applications and strong leadership skills.

Qualifications

• 5+ years in Python with success in productionizing LLM applications.
• Deep knowledge of RAG architecture and advanced prompt engineering.
• Expertise with managed LLM services like Azure or AWS.
• Experience with Docker and CI/CD pipelines.
• Proven ability to lead technical teams and communicate effectively.

Responsibilities

• Design, develop, and deploy Retrieval-Augmented Generation systems.
• Lead the development of advanced generative models for various tasks.
• Select and optimize technology stack for LLM operations.
• Embed observability tools for tracking metrics and performance.
• Hire, mentor, and set standards for ML talent.

Skills

Production LLM Experience

RAG Expertise

Cloud Proficiency

MLOps Acumen

Leadership & Communication

Tools

DSPY

LangChain

LlamaIndex

Hugging Face Transformers

Pinecone

Weaviate

Milvus

Docker

GitHub Actions

Argo

Job description

About the job Lead Generative AI Engineer-Player Coach

Purpose: Own our Generative AI technical vision. You will rapidly prototype and lead a dedicated team of two engineers to launch our company's first intelligent search and content automation systems.

Role Summary

We're looking for a hands‑on Gen AI pioneer who can architect, code, and mentor. This is a "player‑coach" role where you'll be building foundational systems while guiding your team. You will partner daily with product and engineering leadership to transform business goals into cutting‑edge, shippable LLM-powered solutions.

Key Responsibilities

• Architect & Build RAG Systems: Design, develop, and deploy sophisticated Retrieval‑Augmented Generation (RAG) systems to power our next‑generation search and discovery experience.
• Develop & Fine‑Tune LLMs: Lead the development of advanced generative models for nuanced tasks like automated content creation, summarization, and metadata enrichment.
• Own the Gen AI Stack: Select, provision, and optimize our stack, leveraging managed services like Azure OpenAI or AWS Bedrock, or self‑hosting models on GPU infrastructure. You will establish best practices for repo structure, CI/CD, and model/prompt versioning.
• Implement LLMOps: Embed robust observability using tools like OpenTelemetry and Prometheus. This includes tracking standard metrics (latency, cost, accuracy) and specialized monitoring for hallucination, toxicity, and data drift.
• Lead & Mentor: Hire, coach, and develop ML talent. Set the standard for high‑quality code, rigorous experimentation, and rapid iteration within the Gen AI domain.

Must‑Have Skills

• Production LLM Experience: 5+ years in Python with demonstrable success in productionizing LLM applications using modern frameworks like DSPY, LangChain, LlamaIndex, or Hugging Face Transformers.
• RAG Expertise: Deep, practical knowledge of RAG architecture, including advanced prompt engineering, chunking strategies, and proficiency with vector databases (e.g., Pinecone, Weaviate, Milvus).
• Cloud Proficiency: Expertise with managed LLM services (Azure OpenAI Service or AWS Bedrock). Strong foundational cloud skills in either Azure or AWS for compute orchestration (AKS/EKS), serverless functions, and storage.
• MLOps Acumen: Solid experience with Docker, CI/CD pipelines (e.g., GitHub Actions, Argo), and model registries.
• Leadership & Communication: Proven ability to lead small, highly technical teams and clearly communicate complex concepts to stakeholders.

Nice‑to‑Have Skills

• Experience with agentic workflows (e.g., AutoGen, CrewAI).
• Familiarity with multi‑modal models (text, image, etc.).
• Knowledge of advanced LLM fine‑tuning techniques (e.g., LoRA, QLoRA).
• Strong SQL skills (especially with ClickHouse) and a keen eye for inference cost optimization.